Joudaki, Hossein and Rashidian, Arash and Minaei-Bidgoli, Behrouz and Mahmoodi, Mahmood and Geraili, Bijan and Nasiri, Mahdi and Arab, Mohammad (2014) Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature. Global Journal of Health Science, 7 (1). ISSN 1916-9736
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Abstract
Inappropriate payments by insurance organizations or third party payers occur because of errors, abuse and fraud. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors. Ultimately, based on available studies, we recommend seven general steps to data mining of health care claims.
Item Type: | Article |
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Subjects: | Apsci Archives > Medical Science |
Depositing User: | Unnamed user with email support@apsciarchives.com |
Date Deposited: | 04 May 2023 05:35 |
Last Modified: | 29 Jan 2024 06:13 |
URI: | http://eprints.go2submission.com/id/eprint/916 |